Jianhua Joined VISA as Staff Data Scientist in 2018. He has rich experience developing AI solutions for fraud detection using Spark and other tools from end to end. Before Joining VISA, he was a research scientist in University of Phoenix (Apollo Education Group). Jianhua got his PhD degree from Arizona State University, and MS degree (focused on Machine Learning) from Georgia Tech.
AI is becoming omni-present and is influencing the Payments industry in a big way. At VISA, AI driven-products are changing the way we do our business. Merchants are one of the core entities in any payments network. Millions of merchants are observed to be added to the payments ecosystem every month. Some of these are indeed new businesses but a significant fraction, are merchants that have created a new identity with changed attributes. For Visa, it is essential & highly beneficial to have an oversight of how a merchant is in our network. Being able to do so on a continuous basis leads itself to several use-cases as risk-mitigation, loyalty programs etc.
At VISA, we're using AI, big data tools and our suite of internal products to detect merchant changes. Our AI model currently leverages the scale and depth of VISA data along with a suite of AI techniques to track a merchant with very high accuracy. Accuracy and timeliness are of utter importance because not knowing the merchant and its whereabouts can lead to incorrect merchant offers and delays in merchant queries. In this talk, we will share details about our AI model that looks at merchant patterns over regular intervals. We will discuss the specialized data engineering used and several aspects of the model architecture that includes the traditional Machine Learning and consumer behavior pattern based approaches, continuing onto unsupervised learning techniques using near-duplicate algorithms like Locality Sensitivity Hashing from Spark ML.